Imaging systems are used in such fields as microelectronics, medicine, biology, genetic engineering, mapping and even astronomy. The imaging device can be a suitable type of microscope or, in the case of astronomy, a telescope. The demand for image accuracy is high and, therefore, the influence of noise in a signal derived from an imaged object must be minimized.
For reasons of convenience and efficiency, the invention will be described in the microelectronics environment, although another environment could also have been chosen. During the manufacture of very large scale integration (VLSI) semiconductor devices, measurements are made at several stages of the manufacturing process to determine whether particular features on the object are within specified design tolerances. If not, then suitable corrective action is taken quickly.
As is well known, such a manufacturing process produces a wafer which is divided into dies. Each die has a large number of electronic components. These components are defined by what can generally be termed "features" in the sense that a feature is detectable by a microscope as a foreground element distinguishable from a background, or vice versa, and having a dimension such as width. To measure that width the edges of the feature must be located accurately. "Edge" is a term used to signify detectable discontinuities in a signal obtained by imaging the feature (in any environment, not only microelectronics). The goal of edge detection is to accurately locate the transitions despite the influence of blurring and the presence of noise.
As technology has succeeded to increase the component density per die, the feature dimensions have shrunk to significantly below a micrometer. Consequently, the measurement equipment must measure submicrometer dimensions with lower allowable error tolerances.
Automated systems have been developed for making these measurements to replace manual systems in order to obtain higher process yields, to reduce exposure of the wafers to contamination and to provide a higher throughput. One example of an automated system is disclosed in U.S. Pat. No. 4,938,600. As shown in FIG. 1 which is taken from that patent, an image of a feature is recorded through a microscope and the recorded image is then processed electronically to obtain the required measurements. One such automated system is the Model IVS-120 metrology system manufactured by Schlumberger Verification Systems of Concord, Mass., a division of Schlumberger ATE Products. The major elements of the system, including a wafer handler, an optical system and a computer system, are mounted in a cabinet (not shown).
The wafer handler includes a cassette wafer holder 12 which contains wafers to be measured, a prealigner 14, a wafer transport pick mechanism (not shown) for moving the wafers and a measurement stage 18 which holds the wafers during the actual measurement operation. During operation, the wafer transport pick mechanism removes a wafer 16 from cassette 12 and places it on prealigner 14. Prealigner 14 then rotates wafer 16 to a predetermined orientation by sensing a mark, a flat spot or notched edge on wafer 16, after which the wafer transport pick mechanism transfers wafer 16 from prealigner 14 to measurement stage 18 and positions wafer 16 in a horizontal orientation. Stage 18 is movable in three dimensions for precisely positioning wafer 16 relative to the optical system for performing the actual measurement.
The optical system includes microscope 20 and video camera 22 positioned above the measurement stage 18 and wafer 16. Microscope 20 typically has a turret carrying several objective lenses providing a desired range of magnification and is mounted so that microscope 20 and camera 22 have a vertical optical axis which is perpendicular to the wafer surface.
A feature to be measured on wafer 16 is located with microscope 20 in a well known manner by moving stage 18 until the feature is in the field of view of the objective lens. The optical system is focused, and a focused image of the feature is digitized and recorded by the camera 22. The image is then stored or "frozen".
The system is controlled by a computer 30. Coupled to the computer 30 are a monitor 32 for display of the image recorded by the camera 22 and text, and a keyboard 36 (which constitute an input terminal for entering operator commands) and a disk drive 38 for storing system software and data.
Image processor 28 uses software algorithms to locate the edges of the selected feature and make a measurement. Computer 30 then displays the measurement data on screen 32, prints a hard copy or transfers the data directly to a host computer (not shown) for centralized data analysis. Once the process is complete, wafer 16 is returned to cassette 12 by the wafer handler.
The just-described system performs its task of edge detection very well. Image processor 28 determines where a discontinuity occurs in the gray level of the digitized image. Such a discontinuity can occur for any one of many well known reasons to create an edge of a feature. For example, an edge can occur where two materials meet which have different gray levels, or due to topology of the imaged surface. However, as is well known, the digitized image is subject to spurious noise from various sources. For example, variations in the gray level due to noise can be caused by surface imperfections on the die, such as spots and cracks. This noise in the imaged signal can have a significant distorting influence on the accuracy with which the edge is detected, particularly with the ever increasing precision which such automated measurement systems must provide. (Of course, in environments other than microelectronics, there are analogous causes of noise.)
Certain approaches are known which aim to eliminate the noise created by these imperfections and thereby improve the signal-to-noise ratio (S/N). For example, smoothing filters are commonly used for noise reduction. However, as explained in Digital Image Processing by Gonzales and Woods, Addison-Wesley Publishing Co. 1993 at page 191, a smoothing filter blurs edges because it relies on neighborhood averaging which averages all the pixels in an area of selected size around a pixel of interest. Such a blurring of the edge cannot be tolerated in a measurement system which must locate the edge precisely. For such an application, the authors recommend an alternative approach which uses median filters. This approach replaces the gray level of each pixel by the median of the gray levels in a neighborhood of that pixel, instead of by the average. This method is particularly effective to preserve edge sharpness when the noise pattern includes strong, spike-like components. However, even median filtering is not satisfactory for the type of precision measurements discussed above because when the filter parameters are set to provide filtering, the edge gets modified, and when the parameters are set to preserve the edge, the filtering effect is reduced or even eliminated.